Manuscript Topics

With the developments of the modern complex networked systems, such as wireless communication, industry 4.0, internet of things, etc., the traditional problems have to be re-investigated to deal with the complex networking environment, e.g. modelling, control, estimation, optimisation, measurement, reliability analysis, etc. Recently, the new technologies have been presented based on dynamic data learning. How to analyse the uncertainties in dynamic networked systems from the view of data learning becomes a hot theme influencing many research fields.

In particular, the uncertainties cannot be ignored even if the models of the systems have been established. Since the non-linear dynamics widely exist in the networked system, the performance of the entire system will be strongly affected by the systematic uncertainties due to the non-linearity. Considering the randomness of the networking attacks and the sensory measurement, the complexity of the system design and analysis is increasing in terms of cybersecurity, fault diagnosis, performance enhancement, networking synchronization, etc. Note that the accurate model is difficult to obtain and the features of the systems mostly reflected by the measured data. Combining the data learning approaches, some new results should be developed and contribute to the following research topics but are not limited to:

• Internet of Things
• Digital Manufacturing
• Sensor Networks
• Fault Diagnosis
• Cybersecurity
• Wireless Communication
• Stochastic Modelling
• Signal Processing
• Reliability Analysis
• Performance Assessment
• Stochastic Distribution Control
• Data-driven Optimisation
• Artificial Intelligence
• Human-Robot Interaction
• Multi-agent Networking

Papers submitted to this issue are expected to provide original contributions, new solutions, improvements to existing solutions, and practical applications in networked systems. All the contributions should consider the uncertainties into the system design and analysis.

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